Images of JIP4 KO cells with LAMP1-GFP endogenous tag at 3 different z-planes. Scale bar = 10 µm.
Wow these look crazy! Very prominent phenotype!
313 Matching Annotations
- Oct 2025
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www.biorxiv.org www.biorxiv.org
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Immunoprecipitation of CTNS-FLAG and the K->A mutant revealed that WT CTNS-FLAG is modified by HA-Ubiquitin, but the K->A mutant is not. Additionally, after normalizing for the reduced abundance of CTNS-FLAG in JIP4 KO cells, CTNS-FLAG was more ubiquitylated in the absence of JIP4
It looks like in figure 3b that the K->A mutations blocking ubiquitination also rescued the levels of CTNS in the JIP4 KO cells, right? The phrasing here is maybe a bit confusing.
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his pulse-chase strategy revealed that CTNS-RUSH initially trafficked similarly in both WT and JIP4 KO cells as it sequentially exhibited an ER-like distribution before biotin+CHX, a Golgi-like distribution after 1h with biotin+CHX, and a predominantly endosome-like punctate distribution after 2h biotin+CHX. However, after 4h, CTNS was abundant on lysosomes in WT cells but difficult to detect in JIP4 KO cells (Fig. 2k-m). This result indicated that, in the absence of JIP4, CTNS is rapidly degraded upon arrival at lysosomes.
Does this mean that WT and JIP4 KO cells had similar levels of initial expression of CTNS? No defects in transcription/translation/etc.? I assume yes, but may be worth explicitly saying, because since JIP4 is somewhat related to the MAPK pathway, my first thought was that this was related to signaling.
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ystine, the oxidized form of the amino acid cysteine that is predominantly found in lysosomes, accumulated in JIP4 KO cells by over 10-fold, and cysteine accumulated by over 5-fold (Fig. 1e-g; Schulman et al., 1969; Gahl, Bashan, et al., 1982; Abu-Remaileh et al., 2017). However, no other metabolites showed comparable changes.
This figure is super striking!
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These findings broaden understanding of lysosomal function, define a new pathway that results in lysosomal storage disease and shed light on how aberrant cystine storage may contribute to human disease arising from JIP4 loss-of-function mutations.
This is a really interesting mechanistic dissection of the underlying cause of CTNS-involved defects that includes the uncovering of a potential novel disease driver and target, JIP4! Very thorough study and lots of useful information!
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- Sep 2025
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www.biorxiv.org www.biorxiv.org
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Coronal sections of the brains from seven-month-old 5xFAD/PPT1+/- mice were analyzed for amyloid plaque burden using anti-amyloid antibodies (HJ3.4). There is an obvious (Fig. 1C) and statistically significant increase (Fig. 1D) in Aβ plaques in the 5xFAD/PPT1+/- mice compared to the parental 5xFAD strain.
This is very striking data! I'm curious if you looked, or if you know, whether the PPT1 heterozygous mice (the black line in figure 1B) have plaques? Edit: I see that you're addressing this somewhat in the next section, but I'm still curious about the plaques if you know!
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Collectively, these data suggest that haploinsufficiency of PPT1 alone favors an amyloidogenic program.
Really interesting.
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Lysosomal enzyme genes not identified in the human genetic analysis
These were not identified in your initial analysis of the 44 lysosomal enzyme genes and the CEU AD patients or in the proteomic analysis with the 3 late-stage patients, or both?
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44 lysosomal enzyme genes
How and why did you pick these 44? Also, I may have missed it but do you list anywhere what these 44 genes are?
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Collectively, these data strongly suggest that heterozygosity of multiple lysosomal enzyme genes represent risk factors for AD and may identify precise therapeutic targets for a subset of genetically-defined AD patients.
This is a really interesting and well-done paper connecting lysosomal proteins (often involved in lysosomal storage disorders) to AD. I think it opens up a lot of possibilities for future directions in relation to AD, LSDs, and the connection, and I'm excited to see what's next!
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www.biorxiv.org www.biorxiv.org
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This simple single plate protocol allows itself to a wide range of high-throughput research and development screening applications, ranging from streamlining protein production and identification of activity enhancing mutations, to ligand screening for basic research, biotechnological and drug discovery applications.
This is a really interesting method using a peptide tag to target proteins to extracellular vesicles for ease of isolation in E. coli! I can think of lots of benefits and applications!
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As an illustration, we have developed a multiwell format in vitro assay that allows researchers to measure the activity of in-plate expressed and exported VNp-uricase protein (Figure 3), by following changes in 293 nm absorbance to monitor enzyme dependent breakdown of uric acid
I'm guessing that you measured this in your initial paper, but might be worth mentioning here as well. Have you shown that the VNp tag doesn't affect enzyme activity, stability, folding?
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5 min spin
At what speed?
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The VNp system has the additional benefits of not only allowing expression of more challenging proteins, but also expressing proteins at higher yields than standard methods will normally allow
I'm sure you talk about this in your previous paper, but it would be interesting to know the range of proteins that you've tested with this method since you talk about the system being useful for more challenging proteins.
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Schematic of vesicle cleavage and plate-based assay.
So you isolated the vesicles, lysed them, and then went straight to the assay, or did you do any sort of affinity purification or centrifugation or anything here?
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The VNp tag facilitates the export of recombinant proteins into extracellular membrane-bound vesicles, creating a microenvironment that enhances the solubility and stability of challenging proteins
Very cool!
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However current methodologies require extraction and purification of protein from cells requires that often require manual input to the process, and thus roadblocks to the HTS process.
A very minor thing, but this sentence seems a bit odd.
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www.biorxiv.org www.biorxiv.org
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Here we developed CF2H, a rapid and simple workflow for high-affinity binder screening. Our system was designed with the aim of making binder screening simpler, more affordable and accessible to the community. By combining an E. coli lysate-based cell-free system compatible with expression from linear DNA templates to a two-hybrid approach, we constructed a workflow that bypasses tedious cloning, culturing and sequencing steps. Our experimental setup only requires a set of pipettes and a microplate reader for GFP measurements, and its complexity is comparable to setting up PCR reactions.
This is a really interesting method and was a super fun read! I like that not only can it clearly accelerate binder studies, but it can be done in any lab. I'm looking for a use case to try it out already!
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In addition to binding affinity, differences in expression levels, solubility, and stability among binders likely influence the signal output.
Can you interrogate the effect of these different features on the signal output in this method?
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- Jun 2025
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www.biorxiv.org www.biorxiv.org
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Despite these limitations, our oligo-based cell-free screening workflow is well-suited to a wide-range of protein engineering tasks.
I really like this idea! Not necessarily in this paper, but I'm interested in seeing it stress-tested a bit more in future work. What happens with more complex oligos with more mutations? What happens in different proteins?
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This implies that even with misligation products, the assembly fidelity for the complex 10-oligo assembly is sufficient for cell-free protein screening.
Did you move these ligation products directly into cell-free testing like you did for the previous round, or did you do some kind of purification to eliminate those misligation products?
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< 9 hours using the low-cost Opentrons OT-2
How exciting!
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Specifically, we assayed a common batch size of 96 samples consisting of 6 replicates of 14 mutants, a missing oligo assembly, and a no DNA control.
Did you include a WT that you assembled similar to how you assembled the mutants?
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www.biorxiv.org www.biorxiv.org
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Figure 1
I really enjoyed this paper taking a deeper look at the more disordered regions of AlphaFold structures (that tend to be ignored)! It's interesting to see that there actually is some categorizable structure in those regions and to think about what that might mean for using AlphaFold structures to learn more about proteins!
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4.2. Sequence properties of prediction modes
Are there any residues or combos of residues that are more likely to be in the specific types of structure?
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excluding unphysical due to its rarity.
How rare is this?
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Carbonyl oxygen bonds are frequently pointed in the same direction, rather than alternating as in beta strands. D: Ramachandran distribution for general-case residues in the Q86YZ3 fragment 6 prediction. Outliers are marked in purple. The distribution is highly unusual and clustered in the upper right of the plot, corresponding to an extended but unproteinlike conformation.
It's fine in the downloadable PDF, but in the full text, I think this bit is supposed to be at the end of the figure legend for figure 2. I also think 2D is really compelling! It makes me wonder what these plots would like for the other structure types/behaviors that you note?
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A well-packed and well-predicted core is surrounded by barbed wire and pseudostructure.
Any chance you could indicate the various types of structure in the full structure or even just where B-D are located?
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This was largely an exercise in frustration – sequences that fold stably and behave well experimentally appear strongly correlated with sequences that AlphaFold2 predicts with high confidence. Finding experimental versions of near-predictive regions is rare because most residues deposited in the PDB have high-pLDDT AlphaFold counterparts.
Is this because the "near-predictive" regions are likely to be more dynamic and have a more transient structured state that's not captured as well in experimental structures?
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Markup is green for Ramachandran outliers, red and blue for covalent geometry outliers, magenta for CaBLAM, lime green and yellow for cis and twisted peptide bonds.
It would be super helpful to have a legend on the figure for which colors are which. It's also hard to tell some of the colors apart.
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- Apr 2025
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www.biorxiv.org www.biorxiv.org
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While the study showed that there is good predictive accuracy by the ML between high and low-performance mutants, the prediction between high and medium-performance groups was less accurate, likely due to less distinct sequence differences between these groups.
Do you think this will be consistent across proteins or do you think that some of this is protein-specific? I'm interested to see this method applied to new proteins in the future!
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Low (L, < 6 µM)
Does this also include cells where no apigenin was produced? I'm curious about how cases like that or when the variant is just totally not functionally factor into this analysis.
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Mutations for H group clustered at AA89, AA160-AA174, AA189, AA203, and AA206. In the M group, most of the mutations were located at AA160-AA174, AA189, and AA203. In the L group, most mutations were observed at AA89, AA120, AA138, AA159-AA180, AA188, and AA205.
Interesting that there's quite a bit of overlap between all of these! Does the grouping of mutations to particular regions of the protein have to do with the strategy used to generate variants?
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single mutation event occurrences
Are all of your variants single amino acid substitutions? Do you have variants that have multiple substitutions?
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- Mar 2025
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www.biorxiv.org www.biorxiv.org
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The transmembrane domain protein fused tTA is localised to the plasma membrane, and thus the GFP signal is low in the absence of an active TEV protease, but an active protease cleaves tTA enabling its translocation to the nucleus and induction of GFP expression
Do you have a method to control for or normalize for differences in the expression of your TEV proteases?
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Twenty-three of the 110 selected design monomers could not be successfully cloned, potentially as a result of the instability or toxicity of the synthesised sequence towards the host E.coli cells.
Cloned or expressed? It would be interesting to know more about the ones that you weren't able to produce. Which methods were they from? How different were they from the starting protein? Did the cells die, or did you just get super low yield?
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- Feb 2025
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www.biorxiv.org www.biorxiv.org
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In region A, none of the tested variants were more active than the original Con1.
Do you have confirmation that they're being expressed and folded? It would be interesting to see how well this does for stability instead of strictly enzyme activity.
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Overview of workflow.
I really like this figure!
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We demonstrate CFPS’s role in the protein ML workflow by rapidly assessing 100’s of protease variants for functionality by combining CFPS with an assay for protease activity.
Really enjoyed reading this paper! It was nice to see your example of how one might combine CFPS and ML to evaluate variants!
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homemade CFPS system
I'd love to know more about your homemade CFPS system. I'm assuming you used the Kwon & Jewett paper mentioned in the previous section but I'm not sure it's listed in your references.
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As shown in Figure 6, comparable trends of activity are observed when the variants are screened either directly in CFPS, or as purified proteins at equal concentrations, therefore indicating that trends in variant activity in CFPS are reflecting genuine differences in protease activity.
Could you have just purified the protein straight from the CFPS to directly test the assumption that there's no significant difference in expression levels with CFPS assuming the same starting DNA concentration?
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The mutations in regions A and B and their differing range of activities provide two contrasting sets of data to train the ML workflow on.
It's interesting that you get different results between regions A and B, and I agree that it seems like region A is involved in the enzymatic activity and has less flexibility, but I wonder if you combined mutations from region A + region B if you could get even more improved activity.
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- Jan 2025
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www.biorxiv.org www.biorxiv.org
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Data sharing and exploration for community-driven visual proteomics
How exciting! I can't wait to dig around in this dataset!
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The potential for localizing and resolving different components of the clathrin pathway in action within the cell remains to be explored, potentially using this C. reinhardtii dataset.
This is so cool! Many of the components of the clathrin pathway in mammals are at least present in Chlamydomonas (with varying degrees of similarity/identity) so that could be a good place to start. And in addition to looking at those components, I would love to see the maturation of the vesicles from bud to mature vesicle. Do you actually see cases in your dataset where there's budding at the membrane that could be called endocytosis?
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- Dec 2024
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www.biorxiv.org www.biorxiv.org
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First, evolutionary variation has been thought to mirror protein dynamics generally, but we show this similarity exists only when non-functional constraints dominate. Second, active site structural conservation has been attributed to functional constraints alone, but we show it stems largely from their location in rigid regions where non-functional constraints are high.
This is a really interesting paper looking at how different constraints contribute to evolutionary divergence within enzymes! There are some important findings that I'll definitely keep in mind when working with enzymes in the future.
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AlphaFold
I wonder if you expanded outside of the PDB and used predicted structures if you would find similar trends. It could even be a useful way to evaluate AlphaFold.
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While we could have detected and filtered out these cases by examining active site structural differences, doing so would have introduced circularity in our analysis of active site conservation. Instead, their detection as statistical outliers serves to validate our analytical approach by showing it can identify cases that violate its assumptions.
I like the inclusion of these outliers and the discussion about why they may be outliers.
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Our results show that indeed, much of their enhanced conservation is explained by their lack of flexibility.
I think this is such a cool finding! I'm wondering if there are there any cases where the active site is in the more flexible region of the protein? Or if instead of using distance to the active site you used distance to the least flexible region (especially if it's separate from the active site)? That could maybe help sort this out even more.
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Section ??)
Just pointing this out!
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Thus, active sites are more conserved than average, but their conservation cannot be attributed solely to functional constraints. These results show that much of their conservation is due to their location in rigid regions, indicating the need to reconsider the assumption that active site conservation arises exclusively from functional constraints.
Very interesting!
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As the relative contribution of distance increases, protein size decreases (from 322 to 107 residues) and mean distance to active site shortens (from 15.3Å to 9.8Å). These changes align with the shift from flexibility to distance contributions. The spread of local flexibility, sd(lRMSF), which we showed drives flexibility contributions, shows no consistent trend in these cases. This pattern illustrates our earlier finding that the spread of flexibility and mean distance from the active site explain only 38% of variation in relative contributions between constraints, with the remaining variation potentially arising from other factors such as the variation among families of selection pressure.
I may have missed this, but it would be interesting to see if these trends hold for all protein families studied.
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www.biorxiv.org www.biorxiv.org
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whereas the HyperMPNN construct exhibited a lower level of soluble yield in contrast to the parent and ProteinMPNN sequence (I53-50B.HMPNN: 0.4 mg/L culture; I53-50B: 20.4 mg/L culture; I53-50B.PMPNN: 25.8 mg/L culture)
It's amazing that you made such a thermostable protein this way, but the decreased yield seems like it could be a limitation. Not in this study necessarily, but in the future, it would be interesting to know if proteins created this way do tend to have a lower level of soluble yield?
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The HyperMPNN and ProteinMPNN designs remained stable at 95°C (Fig. 5C and S4), indicating a significant improvement in thermal stability.
Cool!!
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owever, we saw no substantial difference in the number of salt bridges between proteins from E. coli (median 16.2) or hyperthermophiles (median 17.0). Intriguingly, for redesigns of E. coli proteins the ProteinMPNN designed sequences only had a median of 8.8 salt bridges per protein compared to the 17.0 median of HyperMPNN (Fig. 4B).
This is interesting! Do you have any thoughts about this? Maybe ProteinMPNN has a bias towards less salt bridges in general?
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mesophile E. coli
I'm curious how the protein content between the two organisms compare? Do they have many of the same proteins just optimized for high temperature vs not?
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It could be observed that the core of proteins from hyperthermophiles has 4.4% more apolar residues than the E. coli reference. For the surface, proteins from hyperthermophiles had a 3.9% increase in positively charged residues, a 4.1% increase in apolar residues, a 4.6% reduction in polar residues and a 4.6% reduction in others. For the core, an 4.4% increase in apolar residues in proteins from hyperthermophiles was observed.
It's a bit hard to put these percentages into context. I wonder if having a graph that shows the actual values for each group instead of just the difference would be helpful. Like I'm curious how much variation there is on a protein-to-protein basis and how significant these differences are in relation to that.
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‘
Tiny comment, but I think you're missing the closing apostrophe here!
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- Oct 2024
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www.biorxiv.org www.biorxiv.org
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Among the 12 most highly ranked features across protein families are hydrogen bonds (MI=0.775), total surface tension (MI=0.763), london dispersion forces (MI=0.758), repulsive interactions (MI=0.722), internal tension (MI=0.708), ASA (MI=0.694), hydrophobic contacts (MI=0.561), TG frequency (MI=0.562), internal hydrophobicity (MI=0.561), VN frequency (MI=0.556), total hydrophobicity (MI=0.539), and GG frequency (MI=0.509).
This is really interesting! I think it could also be interesting to see if any of the features (these or others) correlate or if any features could be predictive of others?
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Here we present InteracTor, a new toolkit for the extraction of three types of protein feature encodings: interaction features, physicochemical features, and compositional features.
This is super cool! I can't wait to try it out!
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Extract atom, residue, and sequence information from PDB file (Figure 1A): This step involves parsing the Protein Data Bank (PDB) file to obtain the atomic types, 3D coordinates, and the amino acid sequence of the protein
I'm curious if you can use this with structures predicted by AlphaFold or ESMFold. Related to that, I'm curious if you need to do any sort of pre-processing of the structures (mostly for AlphaFold and ESMFold structures because they're known to not always have optimal side chain placement).
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A)
I think this figure might also be mixed up.
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A)
I think I only see one panel in this figure
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- Sep 2024
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www.biorxiv.org www.biorxiv.org
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Code for algorithms and figures is available at https://github.com/ronboger/conformal-protein-retrieval/.
Thanks for providing the code! It helped me better understand some of the examples in the paper.
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Although we extensively use Protein-Vec in this work, our approach is model agnostic and can be used with any search algorithm.
Most of your examples seem to be embedding or vector-based, which is very cool. But I think it could be useful to see some examples that use sequence or even structures since that is also presumably doable with your approach.
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3.3 DALI prefiltering of diverse folds across the proteome
I really love this example!
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We find that 39.6% of coding genes of previously unknown function meet our criteria for an exact functional match
I'm having a bit of trouble separating what your approach enables vs what just Protein-Vec alone does in this example. I know that your approach tells us about confidence in the annotations, but it might be interesting to discuss what comes out of Protein-Vec alone vs what comes out with your approach?
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Structural alignment between predicted structure of functional hit of previously unannotated protein in Mycoplasma mycoides and characterized exonuclease.
Might have missed this, but which proteins are which color?
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Our framework enhances the reliability of protein homology detection and enables the discovery of new proteins with likely desirable functional properties
I think that the idea of using conformal prediction to generate some sort of confidence about which proteins to experiment with could be extremely useful! I really enjoyed reading this paper, and one of my favorite things about this paper is that the authors include so many different examples of how this could be applied. I think it could be very cool to take some of these predictions into the lab in the future!
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For example, a recent work Protein-Vec [24] presented state-of-the-art results across numerous benchmarks for function prediction.
Because you use Protein-Vec quite a bit throughout this paper, it might be useful to give a bit more context up front.
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- Aug 2024
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www.biorxiv.org www.biorxiv.org
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Protein expression and purification
I am curious if there were differences in expression or yield between the different proteins. I could see that being important if the thought is to produce a bunch of this protein and use it to degrade PET.
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The position and orientation of the catalytic triad (D210, H242, and S265) overlaps perfectly with the catalytic triad in the parent enzymes
This is interesting! Do the protein design methods you used specifically try to preserve the catalytically active parts of the protein or was this something that you assessed when picking proteins? If not, might that be useful to consider when selecting proteins to test? For example, maybe the proteins with negligible activity had a lot of differences in the triad. That seems like something you could catch before purifying and testing activity.
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Conclusions
I'd love to see some discussion around your working hypothesis as it seems that you maybe disproved it? It might even be cool to have a bivariate plot with thermal stability on one axis and enzymatic activity on the other to see if there is a correlation. Additionally, because you tried 3 different methods to generate proteins and then evaluated them the same way, it might be useful to talk about which worked best, why that might be, pros/cons of the three methods, etc.
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data not shown
Again, it would be interesting to see this data, even if you just put it in the supplement!
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data not shown
This sounds like such a cool result, it would be great to be able to see the data.
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For the P06 and P08 variants, we did not set out to determine their melting temperature because of their negligible enzymatic activity
It might still be useful to determine the Tm for these proteins to help with testing the working hypothesis that you discussed in the intro that proteins with higher stability have higher enzymatic activity.
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- Jul 2024
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www.biorxiv.org www.biorxiv.org
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The results validated the computational model, concluding that this domain is predominantly helical in nature. The confidence built by this study now pushes us to move ahead in order to solve the atomic structure of this critical domain by crystallography or NMR spectroscopy, which in turn will decipher the exact mechanism by which this essential protein engages DNA to cater to various functions
What a cool paper combining computational modeling of protein structure with experimental analysis to support it! I'm excited for the next steps listed here (crystallography or NMR) to see how those results line up with what was found here, but either way, I'm a big fan of the combined computational and experimental work!
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we used Raman spectroscopy. Raman spectrum of the buffer (control) is displayed in black, whereas that of the Myb domain is displayed in red
Did you do technical replicates? I'm curious about how consistent the data is between trials.
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Graphical quantification of intensity of the protein-DNA complex formed
I'm curious if you happened to do replicates of this and/or stats to determine if your quantification is significant?
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(Accession Number: Q62187)
Love that you included the accession number and a direct link to the sequence! It can be a pain when papers don't reference the exact protein that they're working with. It might be worth explicitly stating how you identified the Myb domain.
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AlphaFold, SWISS-MODEL, and Robetta predicted compact and ordered structures with a very high percentage of α-helical conformations. While the model predicted by I-TASSER was less compact and ordered compared to the models generated by the above servers. All models have very similar and reliable statistics as per the overall SAVESv6.0 results. In summary, the structural integrity and statistics of the models derived from both homology and ab-initio methods showed considerable consistency. This confirms the reliability of Robetta and other models (excluding I-TASSER) for further computational analysis.
I really appreciate the direct comparison of the 4 methods on a single sample. This is totally beyond the scope of the paper, but I wonder if these observations would hold true with other proteins/protein domains.
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27%
Is this not just because you're BLASTing a single domain against a database of mostly full proteins?
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protein
It's a bit confusing to refer to the Myb domain as a "protein" when you aren't talking about the full-length protein.
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neither an in silico nor a physically determined structure of the individual domains of TTF1 is available to date
Just curious if there's a structure available of the full protein? It might be useful context to see how your analysis of the Myb domain fits with the full structure (if it's available).
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DDB1
Do you know the domains that these different interacting proteins bind? The first sentence suggests that you do, and it might be useful for better understanding the particular domain that you focus on here (Myb) to know what interacts with it.
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ranging from 323 to 445 amino acids
This is very minor, but when I first read this (before looking at Figure 1), I thought this phrase meant that this specific region was 323-445 amino acids long. It might be less confusing to use language like "a specific region located between amino acid 323 and 445"? Otherwise, this section introducing the functional domains is very thorough and got me right up to speed on TTF1!
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- Jun 2024
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www.biorxiv.org www.biorxiv.org
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Introduction
This is an interesting deep dive into the ADF family, particularly in Arabidopsis! There's a ton of data and I appreciate the open code!
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Dataset S1
Are these datasets provided somewhere? I might have missed them, but couldn't find them!
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Fig
Are motifs 3 and 6 related since they're both green?
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The longest genome sequence is ADF (AT3G45990; Putative), at 3,359bp, and the shortest is 945 bp (ADF11). The average length is 1,490 bp for all ADFs. Next, we analyzed the physicochemical properties of ADF proteins, including amino acid length, molecular weight (MW), theoretical pI, aliphatic index, and the grand average of hydropathy (GRAVY) (Table 1). The amino acids length of each ADF was found to be similar, ranging from 133 to 150. The molecular weight varied from 15,820 kDa (AtADF7) to 17.942 kDa (ADF2), while the GRAVY of all ADF genes were below zero. The maximum aliphatic index value was 83.53 (AtADF4), and the minimum value was 70.07 (AtADF6). The results of hydrophilicity and hydrophobicity analysis indicated that all ADF family proteins are hydrophilic proteins.
Can you draw any sort of hypotheses from this analysis? It's a lot of data and I'm just wondering exactly what it all means in the context of the rest!
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cantates
I think this is maybe supposed to be "candidates"?
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ADF subclass I in Arabidopsis includes ADF1, ADF2, ADF3, and ADF4,
Is it known if any of these ADF proteins have overlapping functions? Can they compensate for another if needed?
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As shown in Fig 2b,
I don't see a Fig 2b.
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divided into three clusters, as shown in Fig. 3a
The clusters aren't super obvious to me, could you maybe highlight the 3 clusters in the figure?
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ADF genes with other homologous genes,
I'm curious how similar these genes are within organisms and between organisms. Do you have sequence alignments or sequence identities you could share to give an idea?
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38 eukaryotic species with 1 bacteria species as outgroup. The protein sequence data of selected species, which presents each phylum of eukaryotes, were download from Ensembl and Ensembl Plants database
Apart from choosing one to represent each phylum of eukaryotes, how did you pick these specific species? You might have this somewhere up ahead, but it could also be nice to have a table of these species and the other set of 8 species.
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With 25,498 predicted protein-coding genes, 69% of these have predicted functions based on sequence comparisons and similarities to known proteins. However, approximately only 9% of the genes have been studied or characterized through experimental methods. The remaining 30% remain without any predicted function (Wigge and Weigel, 2001).
I really like the inclusion of the stats to highlight the need for characterization of these proteins! This is no big deal but the last sentence of this chunk (The remaining 30% remain without any...) has slightly odd wording. Maybe something like "the remaining 30% lack functional annotation or characterization"?
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- May 2024
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www.biorxiv.org www.biorxiv.org
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Our results show that topological defects in the actin order are necessary to shape the head of the regenerating Hydra, supporting the notion that actin topological defects are mechanical organizers of morphogenesis.
This is a really cool study of the role of mechanosensing and actin in regeneration using a very cool model! I also really enjoyed all the microscopy images. Looking forward to learning more about Hydra and actin in future papers!
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To test further the requirement of actin-defects, we next turned our attention to head-regenerating tissues that failed to regenerate under compression
I like the use of these for comparison! It's cool that you have built-in examples of when things didn't work to use for a comparison like this.
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360° light-sheet microscopy confirmed the toroidal topology of the persistent non-regenerative tissues, hereafter called toroids.
Woah cool images!
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Head-regenerating tissues inherited the actin nematic order, with a single topological defect on the basal disc, and longitudinal fibres expanding towards the regenerating wound
It might be helpful to annotate an image with what these different things look like or add arrows to the existing figure for folks who aren't as familiar with looking at these images.
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orientation of head-regenerating tissues impacted the phenotypic distribution more than increasing agarose stiffness
This is somewhat related to a previous comment, but is the orientation of head-regenerating tissues affected by agarose compression? It's not super clear from the data in extended figure 1a, but it seems like it could be somewhat related since at 0.5% AC there's only lateral orientation.
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d,
It seems like there's quite a bit of phenotypic variation even between these 2 examples of biaxial, could this variation be meaningful?
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Uniaxial animals with ectopic tentacles were also observed (25% at 0.5% AC), similar to the ectopic tentacles observed in weak Wnt3 overexpressing mutants
It might be worth showing what this looks like (especially compared to the biaxial ones) in the main figure since you do have a portion of organisms with this ectopic tentacle phenotype.
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e observed that under the softest 0.5% agarose compression (0.5% AC), all head-regenerating tissues oriented laterally
Is this expected? Is it because they're less squished so they have room to orient? Just wondering about the biological significance
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- Apr 2024
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www.biorxiv.org www.biorxiv.org
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In summary, this study advances our understanding of septin distribution and phylogenetic groupings, shedding light on their ancestral features, potential function, and early evolution.
I really enjoyed reading this paper about septin evolution! It opens the door to help us understand more about septins by looking outside of Opisthokonta. The paper is also thorough, and in addition to learning about their newly identified septins, I was also able to learn a lot about septin biology in general. I'm excited to see how this data is used in the future!
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It is interesting to speculate that AspE-type Group 5 septins have retained the ancestral trait to form a homomeric G-dimer using their R-fingers.
This is a really cool section! I love the use of structural analysis + phylogeny to uncover something really cool about the function of these proteins.
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except for Gig2 which appears to be variable in Group 8
Do you have any ideas about the functional consequences of this variability? Do you think it could affect the G-interface dimerization?
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septins
I'm curious how/why you selected these specific species and how you decided which septin to use within each species?
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BLASTP
It sounds like your searches were mostly sequence-based. I know septins can be large and can contain regions of disorder, which may make them difficult to work with structurally. However, I'm curious if you think you might identify more septin sequences using a structure-based search (like Foldseek)?
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Excavata, Archeaplastida, Rhizaria, Heterokonta, and Alveolata
Was your search limited to these taxa or did you search more broadly as well?
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- Mar 2024
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www.biorxiv.org www.biorxiv.org
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Translating the input into a UniProt ID: AlphaFind supports three forms of input: UniProt ID, PBD ID, and Gene symbol. Since UniProt ID is internally used to identify a protein, other forms of input must be translated into UniProt ID using publicly available APIs. For PDB ID to UniProt ID conversion, we use: https://www.ebi.ac.uk/pdbe/api/mappings/uniprot/ and Gene symbol to UniProt ID conversion AlphaFind relies on: https://rest.uniprot.org/idmapping.
One of the main reasons I might use a structural search is if I have a novel protein that maybe isn't in UniProt. I don't know if it's possible with the way your tool works, but it could be a cool thing to think about for the future - is there a way to support user provided or determined PDBs that aren't in UniProt?
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Figure
The examples are really great! It is a bit hard to really see what's happening in the overlays of all the structures. It might be helpful to see overlays for each hit protein with the the input as separate panels or something.
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To address this issue, novel searching tools have been developed, e.g., FoldSeek (6), 3D-surfer (7)or Dali server (8).However their functionality has some substantial limitations: they cannot search through the whole AlphaFold DB, and they rely on predefined fold patterns.
I like that you brought up some of these other tools and described how your tool is different. Are there other benefits that the user might care about? For example, I noticed that the web tool is really fast! This is probably beyond the scope here, but a comparison of these different structure search tools would be useful.
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Limitations
I appreciate the limitations section! I'm curious if there are plans to eventually incorporate the newer version of the AlphaFold database? Also wondering about things like the PDB database itself and the ESM metagenomic atlas?
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https://alphafind.fi.muni.cz.
I really appreciate this super easy to use and fast web application tool for finding proteins similar to an input!
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- Feb 2024
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www.biorxiv.org www.biorxiv.org
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Here, I present a Google Colaboratory-based pipeline, named LazyAF, which integrates the existing ColabFold BATCH to streamline the process of medium-scale protein-protein interaction prediction.
Thanks so much for sharing this tool! I really enjoyed reading about it and can't wait to try it myself.
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Supplementary Note 1
I really love this walkthrough!!
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Figure 3.
You mentioned that several of the top predicted PPIs have been previously experimentally validated. It would be interesting to know which of these annotations predicted by your pipeline are supported by experimental validation. Could you somehow denote this in this figure?
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Figure
Could you include a key with the color scale for the heatmap?
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uggesting that a score of co-folding between protein A (bait):protein B (candidate) sometimes is different from that of a co-folding between protein B (bait):protein A (candidate).
If you run the analysis multiple times, do you see variation between runs or is it only when you switch the chains?
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the sequence of the ‘bait’ and that of a ‘candidate’ joined via a colon.
I'm curious if you're pipeline supports more than 2 proteins. For example, could I put in a handful of proteins to see if they form a complex?
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a ‘bait’ protein
I think based on your analysis that the answer to this question is yes, but can you do multiple bait proteins as well as multiple candidate proteins? Also curious about how many proteins this can handle?
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- Jan 2024
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www.biorxiv.org www.biorxiv.org
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In this work, we focus on applying functional prediction methods based on both sequence and structure to analyze the hypothetical proteins of the pathogenic agents that cause these diseases: T. cruzi (Tcr), T. brucei brucei (Tbr), T. brucei gambiense (Tbg), L. infantum (Lif), L. donovani (Ldo), and L. braziliensis (Lbz).
This is a really cool analysis combining sequence-based and structure-based functional domain prediction! It provides tons of new info about trypanosomatid biology (even giving us some possible new drug targets!), but could also tell us a lot about sequence/structure conservation and how we might leverage this for annotation and drug target ID purposes.
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SEquence-Based Functional Prediction (SEBFP) and Structure-Based Functional Prediction (STBFP) results
It would be interesting to think about how your sequence-based predictions and structure-based predictions compare. For example, do you have proteins that were similar based on sequence but not structure and vice versa? And even between different sequence-based and structure-based methods since you applied a bunch of tools? This could be an interesting opportunity to learn more about sequence and structure conservation in an organism that's more distant from humans!
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Structure-Based Functional Prediction (STBFP) results, which identified the IFT70 protein (PDBid: 4UZY) [100]. IFT70 is present in the IFT train which is a crucial component of the intraflagellar transport protein complex responsible for cyclogenesis, an evolutionarily conserved transport process involving the bidirectional movement of particles within cilia [101].
It might be useful to put this information sooner. I was confused why you were talking about IFT70 previously and this context was really helpful!
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In the upcoming sections,
Because it seems like you're sort of starting a new section here, it might be useful to add a title so that it doesn't run together with the previous section.
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our alignment results indicate a higher identity of 50% between these species, with the lowest being 44% between Tcr and Lif.
I love this bit of information about what you found in your analyses. It's very helpful for thinking about these proteins.
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TRX
Would also mention what this abbreviation stands for in case you have readers that aren't super familiar with this protein domain.
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AAA 18 domain
Again would be interesting to include what you found in your analysis about AAA 18 domains.
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ACBP
Might be good to include what this abbreviation means when you first reference it. Would also be useful to include some information about what you found in your analysis that led you to include this section.
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Another server
I'm sure you considered it, but you could also try employing Foldseek to search for matches in a couple subsets of the AlphaFold database as well as CATH50, MGnify, and others. It's also super fast! https://search.foldseek.com/search
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we identified the UFC1 and Ufm1 domains, both of which play roles in the ubiquitination process
It would be cool to see a figure showing how similar the sequences and structures are of these proteins compared to known versions. Since it seems like we know quite a bit about them (ex that muts like Arg23Gln can affect binding in UFC1), it would be interesting to see if particular important residues are conserved.
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the same function must be predicted in at least eight SEBFP tools and two STBFP tools
Why these criteria? Also by requiring that you get matches in both the sequence based and structure based tools, are you missing proteins that maybe have very similar structures (and possibly function) but very different sequences?
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ouch
Should this be "out"?
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- Dec 2023
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www.biorxiv.org www.biorxiv.org
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The differential regulation between actin structures allows us to specifically target and deplete each structure for our investigations.
Very cool! I love studies taking advantage of this differential regulation to assay how different parts of the actin cytoskeleton contribute to cellular functions!
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A.
I wonder if the intensities of Cdc42 are different in these various treatment conditions? for example, it looks like there's lots more signal in the for3 mutants, but this could just be the images shown here. Additionally, the changes in the level of nuclear localization here are interesting!
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indicating that linear actin cables do not facilitate anticorrelation between the two ends
Maybe it's just the individual cells in the image, but the oscillations, while still present, do look somewhat different in the for3 mutants. For example, according to the red arrows, the frequency of oscillations between the DMSO treated and for3 mutants do not look the same in Figure 1A. I agree with Cameron that showing something like supplemental figure 1A would be super helpful, because with that you can really see the difference between DMSO treated/for3 mutants and CK-666 or LatA treated cells.
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- Nov 2023
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www.biorxiv.org www.biorxiv.org
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Abstract
Really fun paper that digs into protein function with a lot of cool assays! Overall, I really enjoyed reading it!
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The presence or absence of the CTD in the CsoS2 and wtMR constructs is a proxy for CsoS2A and CsoS2B, suggesting that these two proteins may contribute differently to the physical properties of the nascent carboxysome.
Could it also be related to the lack on NTD in the wtMR construct?
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Repeat 7 was left out of the wtMR construct because it occurs after the ribosomal slip site in Repeat 6, in an effort to eliminate potential confounding variables between the CsoS2A and CsoS2B isoforms.
I understand the effort to eliminate confounding variables. However, based on the western blots in Fig S3, it seems like in most cases in your strains, you're ending up with CsoS2B (which I think is the long isoform?), but here you're forcing all mutants to the short version? Or maybe I'm misunderstanding! Either way, this part is a bit confusing and I'd love a bit of clarification here.
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ysteines play a non-essential structural role that strengthens the overall integrity of the complex, but may not be necessary for its assembly or function.
Do you think this would be the case if you had mutated the cysteine to an alanine?
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Fig. S2
This is super helpful and it might be worth moving some version to the main text or adding a table of your mutants or something since you have so many and it can be a little tricky to keep track of.
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All strains expressed similar amounts of CsoS2 (Fig. S3), though it should be noted that only CsoS2B was detected; it is likely that expression from the neutral site instead of the native operon reduced ribosomal frameshifting responsible for the production of non-essential CsoS2A.
Would be great if you could quantify these results? I also think it could be helpful to check all mutants with both antibodies to compare 2B to 2A for all instead of just the VTG mutants.
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C to S
I totally see the reasoning behind mutating to C to S instead of A. However, I think it would also be interesting to see if mutating this to an A would cause further functional changes, especially because you changed the rest to A.
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Although both carboxysome lineages contain scaffolding proteins, these proteins are related in function alone; they have no sequence or structural similarity.
This is super interesting! Would love some more information - could you provide citations and/or data to demonstrate this?
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www.biorxiv.org www.biorxiv.org
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Here we discovered that Plasmodium ARPC1 constitutes part of a highly divergent, non-canonical Arp2/3 complex,
I really loved reading this paper! Although actin and the Arp2/3 complex are well-studied proteins in mammalian cells and common model organisms, this paper really highlights how much we still have to learn about both actin biology and especially the Arp2/3 complex!
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However, we could not identify any orthologues to these proteins in the Plasmodium genomes (51). The mode of Arp2/3 activation thus remains to be determined.
This is also something that we encountered in Chlamydomonas! I wonder if a structural search for some of those NPFs might give you better results than a sequence search.
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While the canonical Arp2/3 complex consists of seven subunits, we have only identified five orthologues.
It might be helpful to discuss the roles of the different subunits in actual complex function. For example, ARPC2 and ARPC4 generally form the primary connection with the mother filament and Arp2 and Arp3 help nucleate the new daughter filament. You identified those essential subunits! It might also be helpful to talk a bit about other non-canonical Arp2/3 complexes throughout the tree of life (for example Chlamydomonas), and to talk about previous studies where people have mutated or removed a subunit and the complex still retained some function.
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We therefore conclude that Plasmodium Arp2/3 nucleates actin, much like canonical Arp2/3 complexes
I'm curious if there are any other known actin-dependent phenotypes that you could probe with your ARPC1 null to support this more in future work.
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Arp2/3 complex is highly diverse and thus not bound by the canonical Arp2/3 inhibitor.
Some docking studies using CK-666 could help support this since you already have the structures available!
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CK-666 drastically impaired overall exflagellation rates
I'm wondering how healthy cells were with this dosage of CK-666? And if you tried any concentrations between 50uM and 250uM?
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Including PbARPC1 itself, we thus identified structural homologues to five out of seven subunits of the Arp2/3 complex, including the core proteins Arp2 and Arp3, which suggests the presence of a non-canonical, minimalistic Arp2/3 complex in Plasmodium.
This section is so cool! I like the model of your putative Arp2 and Arp3 with a couple actin monomers. Did you try modeling a full complex with your identified proteins?
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hile we noted that spindle microtubules appeared longer in
Just pointing out that this sentence appears to be incomplete!
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formed axonemes
I'm curious if there are differences in axoneme formation or structure. I'm not sure about plasmodium cells, but the Arp2/3 complex has been found to be involved in flagellar assembly in other organisms.
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In conclusion, our data demonstrate that PbARPC1 is required for normal oocyst growth and sporozoite development, and deletion of ARPC1 leads to a complete block in transmission.
Very interesting!
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The ARPC1 protein sequence is conserved across the genus Plasmodium but shows less than 20% identity to other ARPC1/ARPC1 proteins from model species
I know that you'll get to structure in your work, but was there anything known about structure conservation previously? I know 20% is super low for sequence identity, but I imagine that the structure could potentially be better conserved.
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Previous phylogenetic studies identified Plasmodium ARPC1/ARC40, from here on named ARPC1 to be consistent with the most common Arp2/3 complex subunit nomenclature, as the sole conserved subunit of the Arp2/3 complex in Plasmodium.
Since you mention phylogenetic studies, I'm curious if this apparent loss of most of the Arp2/3 complex is common in Plasmodium's closest relatives or if this is unique to Plasmodium species.
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- Oct 2023
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www.biorxiv.org www.biorxiv.org
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e most important criteria to define TNTs is to assess their transfer function
I really loved this section!
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Tunneling nanotubes (TNTs)
A very cool paper, with some very cool images showing tunneling nanotubes in zebrafish embryos!
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At 200 µM, CK666 appeared to be toxic to the embryos, that were either dying at early stages or, if survived, had observable tail twist at 48 hpf
Is there a third intermediate concentration you could try to see if you get an intermediate effect?
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Figure 3.
Panels C and E and the significance labels are a bit confusing. I'm wondering if there's a different a way to display this data?
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As a complementary approach to confirm the formation of TNT-like structures through a mechanism distinct from cytokinesis, we conducted clonal labeling of zebrafish embryos.
I really love this approach, and I think this data is very cool! I don't know much about these specific labels but I'm curious if you could see transfer of colors between cells at all?
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5 minutes after formation
Could you quantify the lifetime of different types of connections? Just wondering about different ways to classify TNTs vs other types of connections.
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We were able to show that connections in the embryo can be formed from two filopodia-like structures, similarly to TNTs in vitro
I love this figure and video of the two filopodia reaching out and connecting!
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Of interest, a portion of CEP55-negative connections had the length above 10 µm, and they could reach up to 30 µm in length
I'm interested in the thickness of the connections - the bridges seem quite thick.
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o be able to differentiate the TNT-like connections we observed from cytokinetic bridges, we micro-injected lifeAct-mKATE-E2A-CEP55-EGFP mRNA that labels actin and the midbody marker CEP55 in the same cells
This is a clever way to differentiate between cytokinetic bridges and the TNT-like connections, but I don't know that it fully rules out that the TNT-like connections could be related to division. Is there a way to block division and see if you still get TNT-like connections?
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Since TNTs in vitro can have different cytoskeletal composition depending on the cell type 19, in order to further characterize zebrafish TNT-like connections, we injected lifeAct-mKATE-E2A-EGFP-tubulin mRNA that labels actin and tubulin of the same cells (Fig. 1E). Quantification showed that the majority of TNT-like connections contained both actin and tubulin, while about 21% contained only actin
Did you only do this staining and analysis with TNT-like protrusions? It's clear that there's some diversity of the cytoskeletal composition of these structures, but I wonder if compared to other types of protrusions, some pattern might emerge. For example, comparatively, filopodia are likely only actin the majority of the time, making them quite different than these structures.
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TNTs are thin (below 1 µm) and long (up to 100 µm) actin-based membranous connections between cells, that allow for membrane and cytoplasmic continuity
Might be nice to add a citation here since you have specific number values.
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developing embryos
Have these been seen in living embryos before or is your study the first report of this?
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- Sep 2023
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www.biorxiv.org www.biorxiv.org
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close to the interaction cutoff,
I'm sort of interested in the pairs that are close to the interaction cutoff but on the expected side. For example, would a pair with an interaction score of like 0.56 or so have the structure and interaction that you would expect? I guess, how accurate is the 0.5 cutoff?
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The fact that no recycling is required opens the possibility to apply this procedure at large scale
This is really exciting! I can think of tons of really interesting hypotheses that could be tested by doing this kind of analysis at a larger scale, and I think your analysis here does a great job opening that up.
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meaningless because of the disordered parts
Do you see other cases of disorder influencing the predictions of binding? Maybe not to this degree, but are there other cases where disordered regions cause a lower or higher ipTM than expected?
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There is evidence of physical interaction between these proteins, as detected by affinity purification. However, there is no evidence of direct physical interaction by two-hybrid assay.
Might consider adding a citation here
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The 5 models are drastically different from each other
Is this similar to what you see with non-interacting pairs? Are the protein structures themselves quite different or just the contact between the two models?
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I submitted to AF2 prediction a particularly challenging data set from a previous study
I really appreciate the use of this previous dataset for this purpose! I went back to the previous paper, and this dataset seems like the perfect fit for this kind of analysis. I think this is a great test of the limits of this particular feature of AlphaFold2 and provides some great insight into what it can be useful for in the future.
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- Aug 2023
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www.biorxiv.org www.biorxiv.org
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When expressed in PFN1 KO cells, these mutants would sometimes form aggregates inside of mitochondria, causing them to swell and enlarge
This is really interesting, and I'm curious how often these aggregates show up for the different mutants.
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these results suggest a novel function for PFN1 in regulating mitochondria
This paper is so cool and such a fun read! I found the data that showed PFN1 in the mitochondria (figure 6) especially interesting and the mutant data throughout particularly convincing. I'm looking forward to following this story and learning more about PFN1 and how it affects mitochondria!
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Interestingly, the mutants used for this assay have varying effects on PFN1’s ability to operate as an actin assembly factor, from complete to partial loss of function
I think that the mutant data throughout is quite convincing since they have varying actin polymerization activity. It could be really useful for digging into this more to know a little more about these mutants. Do they block binding to other proteins, affect overall profilin folding or expression, or affect other functions that we know of?
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These data show that PFN1 is not located on the surface of the OMM, but rather inside the mitochondria membrane.
This experiment and this data is so cool!!! I am curious if you think actin is also present inside the mitochondria?
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G) Quantification of Parkin foci in PFN1 cells expressing GFP, GFP-PFN1 and GFP-PFN1R88E and the ALS associated mutants M114T, E117G and G118V, showing that rescue was only possible with functional PFN1.
Are the stars here signifying significance between each mutant and the GFP control or the PFN1 rescue?
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Figure 1.
In Figures 1 and 2, I don't think you mention what the stars represent. I'm guessing that they are the usual significance cutoffs, but you might add them in the legends!
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but not mutants deficient in binding actin (Fig. 4M) or those associated with ALS
M114T looks a bit more promising than the others. I'd be interesting to know more about how this mutant is different!
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Decreasing the amount of polymerized actin in control cells by 40-50% with a low overnight dose of Latrunculin A (10-20 nM) (Cisterna et al 2023, in preparation) to approximate the loss of actin caused by PFN1 KO cells, did not result in more mitochondria being delivered to lysosomes (Fig. 2E) or cause an increase in the formation of Parkin foci (Fig. S2).
Does decreasing the amount of polymerized actin further eventually result in mitochondrial defects and activation of mitophagy? In the discussion you mentioned that this process doesn't actually require that much actin so I wonder if the 50-60% that's left is actually sufficient. Additionally, because profilin has such a complex role in actin function (polymerization promotion, monomer sequestering, ATP hydrolysis promoting), I imagine that it is probably quite difficult to mimic what loss of profilin would look like in this way. Is there some other cellular function that is affected by loss of profilin function that you could use as a readout to show that this is affecting cells in a similar way to loss of profilin?
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nterestingly, RNA-seq analysis identified significant changes in expression of genes associated with lysosome/endosome systems and autophagy21 upon the loss of PFN1 expression
I'm interested in knowing a bit more about this expression dataset in the context of this work. Do actin and related proteins express at normal levels in PFN1 KO cells?
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unpainted
unpaired?
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- Jul 2023
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www.biorxiv.org www.biorxiv.org
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indicating atleast a partial sequestration of the Arp2/3 complex
Could you measure the intensity of the Arp3 fluorescence in the cytosol and outside of the aggregates in the HTTQ15 compared to the HTTQ138 cells to see the extent of sequestration? Same with the actin! It might help support some of your conclusions if you can actually point to some numbers and data.
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B) Radial speed (left) (µm/ sec) of the CCSs in HTTQ15 hemocytes as a function of radial distance (in µm) from the cell center obtained from time-averaged PIV analysis (see Material and Methods for details). Polar histogram of distribution of the flow-field directions obtained from PIV analysis relative to the polar direction (see Material and Methods for details) (right). The angles are sharply distributed around a value of 180°, showing the centripetal movement of CCSs. C) Graph (left) showing stalled movement of CCSs in HTTQ138 hemocytes. Polar histogram (right) of flow-field directions obtained similar to the HTTQ15 case in B) gives a broad distribution of the angles, indicating the absence of any directional centripetal movement of CCSs in presence of HTTQ138.
Really striking differences, super cool! I'm curious how many cells were measured to make these graphs and if there were any stats done?
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Together our results indicate that Huntingtin aggregates remodel the cellular actin cytoskeleton in a manner rendering the cells stiffer, where it is unable to assist CCS movement. We further demonstrate that an active remodeling of the actin cytoskeleton can override some of the detrimental effects of the aggregates with respect to endocytosis.
A really interesting paper using some very cool techniques to look at actin (and the Arp2/3 complex!) and clathrin-mediated endocytosis in disease states!
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Our results suggest that due to the increased stiffness of HTTQ138 cells CCS movement may be impaired.
Are there ways to make the cells stiffer without the HTTQ138 defect that could support this statement? Has stiffness been shown to impair CCS movement in previous papers that you could cite here?
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Figure 4.
You might consider some reorganization of figures 2-4 as it would be helpful to see the controls, the knockdown, and the rescue all together in one figure. So for example, putting the Hip1 knockdown and coexpression stuff in the same figure would be helpful so readers don't have to scroll back and forth between figures.
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Together these results indicate that increasing the availability of proteins involved in actin reorganization are capable of restoring CME even in the presence of pathogenic aggregates.
Because you have a lot of proteins that you've found do influence this process, it might be helpful to have a diagram/model maybe at the end showing how each protein is important.
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loss of directional movement
This is a very minor wording thing, but the movement in 2E actually does look quite directional (it's not distributed around the circle), just not the direction that you would expect.
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WT or HTTQ15
This is a bit confusing - are you using WT and HTTQ15 interchangeably or are you showing wild-type in Figure 3A-C instead of of the HTTQ15 expressing cells? I think the HTTQ15 is probably the better control so I'd like to see it in the figure.
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- May 2023
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www.biorxiv.org www.biorxiv.org
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Our results suggest that the vimentin IF network laterally supports microtubules against compressive buckling forces and further helps to structure the microtubule network, thus possibly leading to a more efficient intracellular transport system along the microtubules.
Really interesting study with some beautiful microscopy and analysis! Studies looking at interactions between the different cytoskeletal elements are always great, and I'm left wondering how the third biopolymer, actin, factors in.
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an-alyze
Super minor comment but there are a few places throughout the paper where words are randomly hyphenated in the middle of the word.
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In the cell interior, however, there is a striking difference: in NIH3T3 cells, we observe a strong alignment, whereas in vim-/- cell, there is no such alignment.
Very cool!
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Microtubule and vimentin IF networks in cells on patterns.
It would be nice to see images with just microtubules and just vimentin for each condition.
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the microtubule networks represented in cyan and the vimentin IF networks shown in magenta
This might be beyond the scope of this paper or already done somewhere, but the networks look quite different between the two cell shapes even in just wild-type cells. I'm curious if there are any more in depth characterizations between these?
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We create circle-and crossbow-shaped fibronectin micropatterns, corresponding to unpolarized and polarized cells
Do the cytoskeletal and other components of the cell look mostly normal in these compared to cells not grown on these micropatterns?
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- Apr 2023
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Immunofluorescence staining for α-tubulin showed that the protrusions were filled with microtubules
Do you know if they also typically contain actin based on immunofluorescence or staining?
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